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Record W3015443533 · doi:10.1016/j.jrmge.2019.11.004

Factor of safety of strain-softening slopes

2020· article· en· W3015443533 on OpenAlexafffund
Hossein Rafiei Renani, C. Derek Martin

Bibliographic record

VenueJournal of Rock Mechanics and Geotechnical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Analysis
Canadian institutionsUniversity of AlbertaKlohn Crippen Berger (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsStrength reductionCohesion (chemistry)SofteningGeotechnical engineeringResidual strengthGeological Strength IndexSafety factorShear strength (soil)Factor of safetyMaterials scienceMohr–Coulomb theoryStructural engineeringPlasticityGeologyComposite materialEngineeringRock mass classificationFinite element method

Abstract

fetched live from OpenAlex

Stability analysis of strain-softening slopes is carried out using the shear strength reduction method and Mohr-Coulomb model with degrading cohesion and friction angle. The effect of strain-softening behavior on the slope factor of safety is investigated by performing a series of analyses for various slope geometries and strength properties. Stability charts and equations are developed to estimate the factor of safety of strain-softening slopes from the results of traditional stability analysis based on perfectly-plastic behavior. Two example applications including an open pit mine in weak rock and clay shale slope with daylighting bedding planes are presented. The results of limit equilibrium analysis and shear strength reduction method with perfectly-plastic models were in close agreement. Using perfectly-plastic models with peak strength properties led to overly optimistic results while adopting residual strength properties gave excessively conservative outcomes. The shear strength reduction method with a strain-softening model gave realistic factors of safety while accounting for the process of strength degradation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.181
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2020
Admission routes2
Has abstractyes

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